scholarly journals Self-paced Consensus Clustering with Bipartite Graph

2020 ◽  
Author(s):  
Peng Zhou ◽  
Liang Du ◽  
Xuejun Li
Keyword(s):  
Side Effects ◽  
Bipartite Graph ◽  
Ensemble Learning ◽  
Experimental Results ◽  
Consensus Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Graph Learning ◽  
Robust Result

Consensus clustering provides a framework to ensemble multiple clustering results to obtain a consensus and robust result. Most existing consensus clustering methods usually apply all data to ensemble learning, whereas ignoring the side effects caused by some difficult or unreliable instances. To tackle this problem, we propose a novel self-paced consensus clustering method to gradually involve instances from more reliable to less reliable ones into the ensemble learning. We first construct an initial bipartite graph from the multiple base clustering results, where the nodes represent the instances and clusters and the edges indicate that an instance belongs to a cluster. Then, we learn a structured bipartite graph from the initial one by self-paced learning, i.e., we automatically decide the reliability of each edge and involves the edges into graph learning in order of their reliability. At last, we obtain the final consensus clustering result from the learned bipartite graph. The extensive experimental results demonstrate the effectiveness and superiority of the proposed method.

scholarly journals Robust Multi-view Learning via Half-quadratic Minimization

2018 ◽  
Author(s):  
Yonghua Zhu ◽  
Xiaofeng Zhu ◽  
Wei Zheng
Keyword(s):  
Error Estimation ◽  
State Of The Art ◽  
Data Distribution ◽  
Experimental Results ◽  
Clustering Methods ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Single View ◽  
Quadratic Minimization ◽  
Quadratic Theory

Although multi-view clustering is capable to usemore information than single view clustering, existing multi-view clustering methods still have issues to be addressed, such as initialization sensitivity, the specification of the number of clusters,and the influence of outliers. In this paper, we propose a robust multi-view clustering method to address these issues. Specifically, we first propose amulti-view based sum-of-square error estimation tomake the initialization easy and simple as well asuse a sum-of-norm regularization to automaticallylearn the number of clusters according to data distribution. We further employ robust estimators constructed by the half-quadratic theory to avoid theinfluence of outliers for conducting robust estimations of both sum-of-square error and the numberof clusters. Experimental results on both syntheticand real datasets demonstrate that our method outperforms the state-of-the-art methods.  

scholarly journals constclust: Consistent Clusters for scRNA-seq

2020 ◽  
Author(s):  
Isaac Virshup ◽  
Jarny Choi ◽  
Kim-Anh Lê Cao ◽  
Christine A Wells
Keyword(s):  
Ad Hoc ◽  
Cell Types ◽  
Consensus Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Data Set ◽  
Multiple Parameters ◽  
Distinct Cell ◽  
Analysis Workflow ◽  
And Performance

1AbstractUnsupervised clustering to identify distinct cell types is a crucial step in the analysis of scRNA-seq data. Current clustering methods are dependent on a number of parameters whose effect on the resulting solution’s accuracy and reproducibility are poorly understood. The adjustment of clustering parameters is therefore ad-hoc, with most users deviating minimally from default settings. constclust is a novel meta-clustering method based on the idea that if the data contains distinct populations which a clustering method can identify, meaningful clusters should be robust to small changes in the parameters used to derive them. By reconciling solutions from a clustering method over multiple parameters, we can identify locally robust clusters of cells and their corresponding regions of parameter space. Rather than assigning cells to a single partition of the data set, this approach allows for discovery of discrete groups of cells which can correspond to the multiple levels of cellular identity. Additionally constclust requires significantly fewer computational resources than current consensus clustering methods for scRNA-seq data. We demonstrate the utility, accuracy, and performance of constclust as part of the analysis workflow. constclust is available at https://github.com/ivirshup/constclust1.

Topical Concept Based Text Clustering Method

2012 ◽  
Vol 532-533 ◽  
pp. 939-943
Author(s):  
Yi Ding ◽  
Xian Fu
Keyword(s):  
Dimensional Space ◽  
Document Clustering ◽  
Text Clustering ◽  
Good Explanation ◽  
Experimental Results ◽  
High Dimensional ◽  
Clustering Methods ◽  
Clustering Method ◽  
Concept Clustering ◽  
To Come

Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. In addition, given a particular clustering result it is typically very hard to come up with a good explanation of why the text clusters have been constructed the way they are. . To solve these problems, based on topic concept clustering, this paper proposes a method for Chinese document clustering. In this paper, we introduce a novel topical document clustering method called Document Features Indexing Clustering (DFIC), which can identify topics accurately and cluster documents according to these topics. In DFIC, “topic elements” are defined and extracted for indexing base clusters. Additionally, document features are investigated and exploited. Experimental results show that DFIC can gain a higher precision (92.76%) than some widely used traditional clustering methods.

Conical SNA using fuzzy k-medoids based on user experience

2021 ◽  
pp. 002072092098849
Author(s):  
Poonam Rani ◽  
MPS Bhatia ◽  
Devendra K Tayal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
User Experience ◽  
Three Dimensional ◽  
Experimental Results ◽  
Dynamic Parameters ◽  
Clustering Method ◽  
Cone Model ◽  
Intelligent Approach ◽  
Conical Model

The paper presents an intelligent approach for the comparison of social networks through a cone model by using the fuzzy k-medoids clustering method. It makes use of a geometrical three-dimensional conical model, which astutely represents the user experience views. It uses both the static as well as the dynamic parameters of social networks. In this, we propose an algorithm that investigates which social network is more fruitful. For the experimental results, the proposed work is employed on the data collected from students from different universities through the Google forms, where students are required to rate their experience of using different social networks on different scales.

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters

2013 ◽  
Vol 321-324 ◽  
pp. 1939-1942
Author(s):  
Lei Gu
Keyword(s):  
Clustering Algorithm ◽  
Experimental Results ◽  
Clustering Method ◽  
Neighborhood Graph ◽  
The Core ◽  
Random Samples

The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.

scholarly journals A simple clustering technique to extract subsets of data for function approximation

2015 ◽  
Vol 17 (5) ◽  
pp. 719-732
Author(s):  
Dulakshi Santhusitha Kumari Karunasingha ◽  
Shie-Yui Liong
Keyword(s):  
Function Approximation ◽  
Prediction Models ◽  
Data Extraction ◽  
Single Parameter ◽  
Subtractive Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Clustering Method ◽  
Data Set ◽  
Functional Relationships

A simple clustering method is proposed for extracting representative subsets from lengthy data sets. The main purpose of the extracted subset of data is to use it to build prediction models (of the form of approximating functional relationships) instead of using the entire large data set. Such smaller subsets of data are often required in exploratory analysis stages of studies that involve resource consuming investigations. A few recent studies have used a subtractive clustering method (SCM) for such data extraction, in the absence of clustering methods for function approximation. SCM, however, requires several parameters to be specified. This study proposes a clustering method, which requires only a single parameter to be specified, yet it is shown to be as effective as the SCM. A method to find suitable values for the parameter is also proposed. Due to having only a single parameter, using the proposed clustering method is shown to be orders of magnitudes more efficient than using SCM. The effectiveness of the proposed method is demonstrated on phase space prediction of three univariate time series and prediction of two multivariate data sets. Some drawbacks of SCM when applied for data extraction are identified, and the proposed method is shown to be a solution for them.

Predicting potential side effects of drugs by recommender methods and ensemble learning

2016 ◽  
Vol 173 ◽  
pp. 979-987 ◽  
Author(s):  
Wen Zhang ◽  
Hua Zou ◽  
Longqiang Luo ◽  
Qianchao Liu ◽  
Weijian Wu ◽  
...  
Keyword(s):  
Side Effects ◽  
Ensemble Learning

scholarly journals Diagnosis of Out-of-control Signals in Multivariate Statistical Process Control Based on Bagging and Decision Tree

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
Jing Jiang ◽  
Hua-Ming Song
Keyword(s):  
Process Control ◽  
Decision Tree ◽  
Statistical Process Control ◽  
Ensemble Learning ◽  
Experimental Results ◽  
Multivariate Statistical Process Control ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Multiple Classifiers ◽  
Control Signals

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.

scholarly journals KOMPARASI METODE CLUSTERING K-MEANS DAN K-MEDOIDS DENGAN MODEL FUZZY RFM UNTUK PENGELOMPOKAN PELANGGAN

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Elly Muningsih - AMIK BSI Yogyakarta
Keyword(s):  
Data Clustering ◽  
Small Data ◽  
Clustering Methods ◽  
Monetary Model ◽  
Clustering Method ◽  
Online Sales ◽  
Rfm Model ◽  
Potential Customers ◽  
Cluster 2 ◽  
Better Than

Abstract ~ The K-Means method is one of the clustering methods that is widely used in data clustering research. While the K-Medoids method is an efficient method used for processing small data. This study aims to compare two clustering methods by grouping customers into 3 clusters according to their characteristics, namely very potential (loyal) customers, potential customers and non potential customers. The method used in this study is the K-Means clustering method and the K-Medoids method. The data used is online sales transaction. The clustering method testing is done by using a Fuzzy RFM (Recency, Frequenty and Monetary) model where the average (mean) of the third value is taken. From the data testing is known that the K-Means method is better than the K-Medoids method with an accuracy value of 90.47%. Whereas from the data processing carried out is known that cluster 1 has 16 members (customers), cluster 2 has 11 members and cluster 3 has 15 members. Keywords : clustering, K-Means method, K-Medoids method, customer, Fuzzy RFM model. Abstrak ~ Metode K-Means merupakan salah satu metode clustering yang banyak digunakan dalam penelitian pengelompokan data. Sedangkan metode K-Medoids merupakan metode yang efisien digunakan untuk pengolahan data yang kecil. Penelitian ini bertujuan untuk membandingkan atau mengkomparasi dua metode clustering dengan cara mengelompokkan pelanggan menjadi 3 cluster sesuai dengan karakteristiknya, yaitu pelanggan sangat potensial (loyal), pelanggan potensial dan pelanggan kurang (tidak) potensial. Metode yang digunakan dalam penelitian ini adalah metode clustering K-Means dan metode K-Medoids. Data yang digunakan adalah data transaksi penjualan online. Pengujian metode clustering yang dilakukan adalah dengan menggunakan model Fuzzy RFM (Recency, Frequenty dan Monetary) dimana diambil rata-rata (mean) dari nilai ketiga tersebut. Dari pengujian data diketahui bahwa metode K-Means lebih baik dari metode K-Medoids dengan nilai akurasi 90,47%. Sedangkan dari pengolahan data yang dilakukan diketahui bahwa cluster 1 memiliki 16 anggota (pelanggan), cluster 2 memiliki 11 anggota dan cluster 3 memiliki 15 anggota. Kata kunci : clustering, metode K-Means, metode K-Medoids, pelanggan, model Fuzzy RFM.

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